Data Quality – An Expanding Ring

I was talking to a customer the other day who is about to embark on a data quality quest.  He asked me to explain my view of a data quality initiative.  I explained to him that data quality is a never ending process.  I said it was like dropping a pebble in the water.  The initial ring is small but slowly expands outward.  The data quality process is similar.  You start small, with one application or one subject area, show some success then look to expand the process with additional applications or subject areas.  Then expand further to additional business units or business functions until you have encompassed the entire enterprise.  Then just when you think you’re done, you need to continue to monitor and repair data because it will degrade over time.

While what I said was true, he said that the business would reject that description out of hand.  Business believes that all IT projects are never ending projects.  It is this thought process that always pitches Business against IT.  Business believes that IT never delivers a finished project.  His analogy is that Data Quality is more like building a building.  It takes a lot of time and effort to get the foundation right, then you need to add the structure, electrical, water, communication, and finishing work.  Then you move in and begin to use the building but you’re not done.

You need to handle tenants as they move in and out.  You need to handle exiting tenants that need to move employees around within the existing space.  You need to accommodate new technologies like moving from a wired to a wireless environment.  You need to keep the building clean, the plants growing and the vending machines full. You need to keep the walls painted and the flooring replaced on occasion.

A data quality initiative is the same.  The foundation is strong and the infrastructure is solid but you need to accommodate new data and applications as they come online.  Also, you need to continually monitor the data to ensure it is fresh and clean and has been tweaked to handle changes that happen within an application or the government decides to legislate a solution to a problem that costs you millions but protects the consumer (so they say).

Then this morning my wife was watching one of the morning news shows.  The story was about how cheap paint is and how you can transform a room for the holidays.  Then, after the holidays, you can just paint over it.  I was trying to find a good analogy for a request from the business but decided not to go there.  I’m sure you might have one in mind.

The key to a data quality initiative it to talk to the business in terms they understand.  I always talk to IT about missing or incorrect values like missing phone numbers.  They understand that but business does not always understand completeness, consistency, duplication records or valid values.  I try to translate from talking about data characteristics to talking about business value.  In the health care industry, I discuss the value of having two percent better payment of claims on the first submission because the provider id is not missing.  To the marketing department, I talk about a three percent increase in the take rate because the mailings are accurate, allowing more pieces to reach the intended recipients.  And the call center is able to connect with more prospects for follow-up because the phone numbers are populated because sometimes (actually most of the time) it effects not only the initial consumer of the data but other processes downstream from the originator.

For more on the steps your organization should take to achieve pervasive data quality, read my recent white paper entitled “Informatica Data Quality Methodology – a Framework to Achieve Pervasive Data Quality Through Enhanced Business-IT Collaboration.”

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